Skill UI

This critical protocol is the ultimate measure for dealing with deeply corrupted, unmaintainable, or structurally rotten code modules. Instead of risky patching, it mandates a controlled, multi-phase process to completely delete the target and rebuild it from scratch. It ensures precise identification of all public interfaces, implicit contracts, and working behaviors (the Preservation List) before any removal occurs, guaranteeing the new implementation matches the original functionality while resolving deep architectural debt.

This discipline establishes a portable, framework-agnostic layer for ensuring type safety at the network edge of any React or React Native application. It mandates a single, typed API client to wrap all network fetching, guaranteeing that raw wire JSON is immediately parsed into trusted, domain-specific types or standardized error objects before entering the application state. This pattern drastically reduces runtime errors and enhances code robustness.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.

This skill performs a comprehensive, read-only, multi-agent review of a git diff or file scope. It launches four specialized sub-agents—covering Regression, Security, Performance, and Contracts—in parallel. The main agent then aggregates, filters, and summarizes critical findings, ensuring the code change is robust, secure, and maintainable. Ideal for pre-commit or Pull Request review workflows.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.

This skill guides users through Phases 2 (Pseudocode Design) and 3 (Architecture Design) of the SPARC methodology. It translates high-level specifications into detailed technical blueprints, covering algorithm pseudocode, module boundaries, API contracts (using DDD patterns), and infrastructure planning, ensuring a robust plan before writing production code.

This methodology establishes a single, authoritative, machine-checkable source of truth for service contracts (APIs or events). It guides parallel development between consumers and providers, ensuring schema stability and preventing field drift or integration surprises. Key artifacts include OpenAPI, AsyncAPI, and JSON Schema, making the contract the primary artifact for all teams to reference.

This skill guides art historians on gathering, weighing, and citing non-visual, documentary, and material evidence for academic articles. It covers citing primary sources (archives, contracts), reconstructing provenance chains, interpreting technical reports, and ensuring that all scholarly claims are rigorously traceable to verifiable sources rather than mere assertion.

A comprehensive guide for revising empirical papers submitted to top-tier Software Engineering conferences (like FSE). This framework emphasizes structuring the paper around a clear SE contribution, treating research questions as contracts, backing every claim with proportional evidence, and rigorously arguing threats to validity, rather than merely listing them. It ensures the paper maintains maximum academic rigor and focus.

This guide provides rigorous guidelines for structuring and validating experimental results in computer architecture papers. It covers essential aspects like declaring simulation methodology contracts, ensuring result reproducibility through manifest files, selecting representative workloads, and performing detailed attribution and sensitivity analyses to distinguish true architectural gains from modeling artifacts.

A comprehensive guide for authors revising technical papers for top-tier conferences like ISSTA. It details the necessary structure, emphasizing strict scope definition, establishing clear evaluation contracts (subjects, baselines, metrics), pairing every claim with concrete evidence, and adhering to specialized review discipline.